• Static display
  • good for publication
  • effective to show very specific pattern
• Interactive display
  • gives end user more flexibility
  • exploratory visualization is convenient

install.packages(c("ggplot2","plotly", "DT", "ggmap", "gapminder"))

• gapminder data is used from gapminder package

library(gapminder)
dat <- subset(gapminder, year==2007)

library(ggplot2)
p <- ggplot(dat, aes(x = log(gdpPercap), y = lifeExp))+
  geom_point(aes(size = sqrt(pop/pi)), pch = 21, show.legend = FALSE) +
   scale_size_continuous(range=c(1,30)) + aes(fill = continent)
p

library(plotly)
ggplotly(p + aes(label = country))

q <- ggplot(gapminder, aes(x = log(gdpPercap), y = lifeExp, frame=year))+
  geom_point(aes(size = sqrt(pop/pi)), pch = 21, show.legend = FALSE) +
   scale_size_continuous(range=c(1,30)) + aes(fill = continent)
ggplotly(q)

library(DT)
datatable(dat)

plot_ly(gapminder, x = ~gdpPercap, y = ~lifeExp, z = ~continent) %>%
  add_markers(color = ~continent)

library(ggmap)
crimes <- read.csv("omaha-crimes.csv")
omaps <- get_map(location = 'Omaha', maptype = 'roadmap', zoom = 11, color='bw')
p <- ggmap(omaps) +
  geom_point(size=5, alpha = 1/2, aes(lon,lat, color=type), data = crimes)
ggplotly(p)

myMap <- get_map(maptype = "satellite", zoom = 8)
p <- ggmap(myMap) +
  geom_polygon(aes(x = lon, y = lat, group = plotOrder),
              data = zips, colour = "black", fill = NA)
ggplotly(p)

• Animation using ggplot2 and plotly